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PubMed1993

[A comparative consideration of para-aortic fields within the framework of the German Hodgkin's Disease Study].

In 33 patients of the multicenter German Hodgkin's Lymphoma Study Group the field borders of the paraaortic field were compared computer-assisted. It was seen that fields are chosen often too small or too large, though precise description of the fields is given in the protocol. In these patients the para-aortics were irradiated exclusively as extended field region. In addition to the above observation the clipping of the spleen pedicle provides the optimal help for correct positioning of this field. Overall a very low frequency of lymphatic clipping is observed, though clinical needs are obvious.

Aorta↗
PubMed1995

Multimedia cardiac catheterization record document system.

In this paper, we present the design and implementation of a multimedia cardiac catheterization record (CCR) system. A traditional CCR contains medical report, electrocardiograms, X-ray images, and possibly video sequences for angiograms. The physicians found it difficult to access and manage these multimedia information easily. Current advances in multimedia technology has made possible the authoring, storage and presentation of these medical information in a systematic and easily-accessible way. This paper discusses the background and rationale that led to the design and implementation of a cost-effective multimedia medical document system framework and prototype.

Cardiac Catheterization↗
PubMed2006

Ubiquitous computing to support co-located clinical teams: using the semiotics of physical objects in system design.

OBJECTIVES: Co-located teams often use material objects to communicate messages in collaboration. Modern desktop computing systems with abstract graphical user interface (GUIs) fail to support this material dimension of inter-personal communication. The aim of this study is to investigate how tangible user interfaces can be used in computer systems to better support collaborative routines among co-located clinical teams. METHODS: The semiotics of physical objects used in team collaboration was analyzed from data collected during 1 month of observations at an emergency room. The resulting set of communication patterns was used as a framework when designing an experimental system. Following the principles of augmented reality, physical objects were mapped into a physical user interface with the goal of maintaining the symbolic value of those objects. RESULTS: NOSTOS is an experimental ubiquitous computing environment that takes advantage of interaction devices integrated into the traditional clinical environment, including digital pens, walk-up displays, and a digital desk. The design uses familiar workplace tools to function as user interfaces to the computer in order to exploit established cognitive and collaborative routines. CONCLUSION: Paper-based tangible user interfaces and digital desks are promising technologies for co-located clinical teams. A key issue that needs to be solved before employing such solutions in practice is associated with limited feedback from the passive paper interfaces.

Computer Peripherals↗
PubMed2004

Multilevel modeling and inference of transcription regulation.

The understanding of transcription regulation is a major goal of today's biology. The challenge is to utilize diverse high-throughput data in order to infer mechanistic models of transcription control. We propose a new model which integrates transcription factor-gene affinity, protein abundance, and gene expression profiles. The model provides a detailed, yet computationally tractable description of the relations between transcription factors, their binding sites at gene promoters, and the combinatorial regulation of transcription. At the core, our model manipulates dose-affinity-response functions that associate transcription factor concentrations and transcription factor-DNA affinities to determine the rate of transcription factor-DNA reactions. We study computational problems that arise in optimizing such models and develop polynomial algorithms for certain problems. We show how to assess missing values (notably protein abundance) and describe a novel framework to infer models from currently available data. On budding yeast carbohydrate metabolism data, our results demonstrate the sensitivity and specificity of the approach. They also suggest new active binding sites and a regulation model for the transcription program of the galactose system.

Algorithms↗
PubMed2004

A framework for scientific data modeling and automated software development.

MOTIVATION: The lack of standards for storage and exchange of data is a serious hindrance for the large-scale data deposition, data mining and program interoperability that is becoming increasingly important in bioinformatics. The problem lies not only in defining and maintaining the standards, but also in convincing scientists and application programmers with a wide variety of backgrounds and interests to adhere to them. RESULTS: We present a UML-based programming framework for the modeling of data and the automated production of software to manipulate that data. Our approach allows one to make an abstract description of the structure of the data used in a particular scientific field and then use it to generate fully functional computer code for data access and input/output routines for data storage, together with accompanying documentation. This code can be generated simultaneously for different programming languages from a single model, together with, for example for format descriptions and I/O libraries XML and various relational databases. The framework is entirely general and could be applied in any subject area. We have used this approach to generate a data exchange standard for structural biology and analysis software for macromolecular NMR spectroscopy. AVAILABILITY: The framework is available under the GPL license, the data exchange standard with generated subroutine libraries under the LGPL license. Both may be found at http://www.ccpn.ac.uk; http://sourceforge.net/projects/ccpn CONTACT: ccpn@mole.bio.cam.ac.uk.

Biopolymers↗
PubMedDate not supplied

Image analysis and computer vision in medicine.

Multimedia lives with images; medical images are born from digital imaging. A physician's multimedia workstation cannot exist without tools for manipulating images, performing measurements and, generally speaking, extracting and collecting pieces of information from the available data. Image analysis and computer vision constitute a wide and rapidly evolving field. This paper is intended as an introductory document for medical imaging researchers and practitioners wishing an overview of recent developments and trends. The major lines of activities in the domain are presented, under the common framework of a processing pipeline. After a presentation of the various stages of this pipeline, current subjects of research are indicated.

Computer Graphics↗
PubMed2005

An adaptive window width/center adjustment system with online training capabilities for MR images.

OBJECTIVE: Adaptive and automatic adjustment of the display window parameters for magnetic resonance images under different viewing conditions is a challenging problem in medical image perception. An adaptive hierarchical neural network-based system with online adaptation capabilities is presented to achieve this goal in this paper. METHODOLOGY: The online adaptation capabilities are primarily attributed to the use of the hierarchical neural networks and the development of a new width/center mapping algorithm. The large training image set is hierarchically organized for efficient user interaction and effective re-mapping of the width/center settings. The width/center mapping functions are estimated from the new user-adjusted width/center values of some representative images by using a global spline function for the entire training images as well as a first-order polynomial function for each selected image sequence. The hierarchical neural networks are then re-trained for the new training data set after this mapping process. RESULTS: The proposed automatic display window parameter adjustment system is implemented as a program on a personal computer for testing its adaptation performance. Experimental results show that the proposed system can successfully adapt its parameter adjustment on a variety of MR images after user re-adjustment and re-training of neural networks. CONCLUSION: This demonstrates the effective adaptation capabilities of the proposed system based on the framework of training data mapping and neural network re-training.

Algorithms↗
PubMedDate not supplied

Computer-based support groups. Nursing in cyberspace.

The focus of this article is on the nurse monitor role in a project whose overall goal is to use telecommunication technology to provide information and support to middle-aged rural women living with chronic illness. The impact of participation in these support groups on the women's psychosocial health is also discussed. The purpose of the project, the underlying conceptual framework of social support, a project overview, project philosophy and protocols, and the role of the Nurse Monitor are described.

Computer Communication Networks↗
PubMed2000

Using Bayesian networks to analyze expression data.

DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the cell. A major challenge in computational biology is to uncover, from such measurements, gene/protein interactions and key biological features of cellular systems. In this paper, we propose a new framework for discovering interactions between genes based on multiple expression measurements. This framework builds on the use of Bayesian networks for representing statistical dependencies. A Bayesian network is a graph-based model of joint multivariate probability distributions that captures properties of conditional independence between variables. Such models are attractive for their ability to describe complex stochastic processes and because they provide a clear methodology for learning from (noisy) observations. We start by showing how Bayesian networks can describe interactions between genes. We then describe a method for recovering gene interactions from microarray data using tools for learning Bayesian networks. Finally, we demonstrate this method on the S. cerevisiae cell-cycle measurements of Spellman et al. (1998).

Algorithms↗
PubMed2026

Simulation of CRISPR/Cas9-mediated gene editing for the Vitellogenin gene in Apis mellifera.

CRISPR/Cas9 genome editing provides a powerful framework for interrogating gene function in Apis mellifera. Yet, empirical application remains challenging due to biological constraints, including haplodiploid genetics, narrow embryonic injection window, and the social rearing requirements that complicate functional validation. These constraints necessitate in silico pre-screening to maximize editing success before resource-intensive wet-lab implementation. Within the omnigenic framework, which distinguishes core regulatory genes from peripheral loci buffered by network effects, vitellogenin (Vg) represents an optimal target which is ancestrally dedicated to yolk provisioning; it has been co-opted to orchestrate diverse non-reproductive functions including longevity, stress resistance, immunity, and social behavior. We developed a computational pipeline to design a list of 57 and 56 candidate guide RNAs (gRNA) for targeted Vg knockout, evaluating candidate sites in both functional exons 2 and 3 based on structural accessibility and frameshift efficiency. Comparative analysis revealed complementary strengths in two top-best candidates from initial target pool of predicted gRNAs. The gRNA targeting exon 2 exhibits weaker secondary structure (ΔG = -0.25 kcal/mol versus -2.10 kcal/mol for exon 3), aligning with empirical evidence that sites with ΔG > -1.0 kcal/mol achieve 2-5 × higher Cas9 binding efficiency. This site yielded moderate frameshift frequency (77.8%; 61.9 percentile). Conversely, the predicted editing outcome for the gRNA targeting exon 3, despite stronger structural constraints, demonstrated superior functional disruption metrics demonstrating very high frameshift frequency (88.3%; 95.2 percentile), high in silico editing precision, minimal microhomology-mediated repair bias, and reproducible outcomes wherein nearly all predicted indels disrupt the coding sequence. Protein structure and domain analyses further predict that frameshift edits will generate a truncated protein missing all downstream functional domains. We recommend parallel empirical validation of both exon 2 and exon 3 targets to resolve the trade-off between structural accessibility (favoring higher editing rates) and frameshift efficacy (favoring complete loss-of-function). This dual-target strategy accommodates uncertainty in in vivo performance while maximizing the probability of generating informative phenotypes. Our in silico framework enables rational CRISPR design in non-model organisms by computationally balancing biophysical accessibility with functional impact, accelerating functional genomics in species where empirical optimization faces substantial biological constraints.

Animals↗
PubMed2000

Statistical alignment: computational properties, homology testing and goodness-of-fit.

The model of insertions and deletions in biological sequences, first formulated by Thorne, Kishino, and Felsenstein in 1991 (the TKF91 model), provides a basis for performing alignment within a statistical framework. Here we investigate this model.Firstly, we show how to accelerate the statistical alignment algorithms several orders of magnitude. The main innovations are to confine likelihood calculations to a band close to the similarity based alignment, to get good initial guesses of the evolutionary parameters and to apply an efficient numerical optimisation algorithm for finding the maximum likelihood estimate. In addition, the recursions originally presented by Thorne, Kishino and Felsenstein can be simplified. Two proteins, about 1500 amino acids long, can be analysed with this method in less than five seconds on a fast desktop computer, which makes this method practical for actual data analysis.Secondly, we propose a new homology test based on this model, where homology means that an ancestor to a sequence pair can be found finitely far back in time. This test has statistical advantages relative to the traditional shuffle test for proteins.Finally, we describe a goodness-of-fit test, that allows testing the proposed insertion-deletion (indel) process inherent to this model and find that real sequences (here globins) probably experience indels longer than one, contrary to what is assumed by the model.

Algorithms↗
PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗
PubMed1975

Long-term value change initiated by computer feedback.

To determine whether value change can be induced by a computer rather than a human agent and whether value change can be induced even when target values are not preselected for experimental treatment, subjects first filled out the Rokeach Value Survey and then obtained information from a computer enabling them to compare their own value rankings with those previously obtained for various reference groups. Experimental subjects exposed to computer feedback showed, first, significant changes in value systems 2 months afterward whereas control subjects showed no such changes. Second, value system change was significantly greater among those experimental subjects whose value rankings were on the whole more discrepant from those of positive reference groups. Third, rankings of certain values concerning achievement, peace, and egalitarianism were affected in a 2-month period following the treatment. These findings are interpreted within a broader theoretical framework about the conditions leading to long-term cognitive and behavioral change.

Adult↗
PubMed2000

Comparing the success of different prediction software in sequence analysis: a review.

The abundance of computer software for different types of prediction in DNA and protein sequence analyses raises the problem of adequate ranking of prediction program quality. A single measure of success of predictor software, which adequately ranks the predictors, does not exist. A typical example of such an incomplete measure is the so-called correlation coefficient. This paper provides an overview and short analysis of several different measures of prediction quality. Frequently, some of these measures give results contradictory to each other even when they relate to the same prediction scores. This may lead to confusion. In order to overcome some of the problems, a few new measures are proposed including some variants of a 'generalised distance from the ideal predictor score'; these are based on topological properties, rather than on statistics. In order to provide a sort of a balanced ranking, the averaged score measure (ASM) is introduced. The ASM provides a possibility for the selection of the predictor that probably has the best overall performance. The method presented in the paper applies to the ranking problem of any prediction software whose results can be properly represented in a true positive-false positive framework, thus providing a natural set-up for linear biological sequence analysis.

Computational Biology↗
PubMed1988

Computer simulations in zeolite chemistry.

Recent developments in molecular modelling techniques have provided us with a new and powerful tool for the simulation and better understanding of the complex chemistry of zeolites. The range of applications include cation distributions, acid strengths of catalytic sites, intergrowth phenomena, derivation of new structures, molecular adsorption and selective diffusion of organic molecules through zeolite frameworks. A perspective summary of a number of simulation methods and their specific areas of applicability is given in this article.

Aluminum Silicates↗
PubMed2003

Simulation in surgical training: educational issues and practical implications.

BACKGROUND: Surgical skills are required by a wide range of health care professionals. Tasks range from simple wound closure to highly complex diagnostic and therapeutic procedures. Technical expertise, although essential, is only one component of a complex picture. By emphasising the importance of knowledge and attitudes, this article aims to locate the acquisition of surgical skills within a wider educational framework. SIMULATORS: Simulators can provide safe, realistic learning environments for repeated practice, underpinned by feedback and objective metrics of performance. Using a simple classification of simulators into model-based, computer-based or hybrid, this paper summarises the current state of the art and describes recent technological developments. Advances in computing have led to the establishment of precision placement and simple manipulation simulators within health care education, while complex manipulation and integrated procedure simulators are still in the development phase. EVALUATION: Tension often exists between the design and evaluation of surgical simulations. A lack of high quality published data is compounded by the difficulties of conducting longitudinal studies in such a fast-moving field. The implications of this tension are discussed. THE WIDER CONTEXT: The emphasis is now shifting from the technology of simulation towards partnership with education and clinical practice. This highlights the need for an integrated learning framework, where knowledge can be acquired alongside technical skills and not in isolation from them. Recent work on situated learning underlines the potential for simulation to feed into and enrich everyday clinical practice.

Clinical Competence↗
PubMed2000

Nitric oxide binding to ferric cytochrome P450: a computational study.

The interaction between nitric oxide (NO) and the active site of ferric cytochrome P450 was studied by means of density functional theory (DFT), at the generalized gradient approximation level, and of the SAM1 semiempirical method. The electrostatic effects of the protein environment were included in our DFT scheme by using a hybrid quantum classical approach. The active-site model consisted of an iron(III) porphyrin, the adjacent cysteine residue, and one coordinated water molecule. For this system, spin populations and relative energies for selected spin states were computed. Interestingly, the unpaired electron density, the HOMO, and the LUMO were found to be highly localized on the iron and in an appreciable extent on the sulfur coordinated to the metal. This provides central information about the reactivity of nitric oxide with the active site. Since the substitution of a molecule of H2O by NO has been proposed as being responsible for the inhibition of the cytochrome in the presence of nitric oxide, we have analyzed the thermodynamic feasibility of the ligand exchange process. The structure of the nitrosylated active site was partially optimized using SAM1. A low-spin ground state was obtained for the nitrosyl complex, with a linear Fe-N-O angle. The trends found in Fe-N-O angles and Fe-N lengths of the higher energy spin states provided a notable insight into the electronic configuration of the complex within the framework of the Enemark and Feltham formalism. In relation to the protein environment, it was assessed that the electrostatic field has significant effects on several computed properties. However, in both vacuum and protein environments, the ligand exchange reaction turned out to be exergonic and the relative orders of spin states of the relevant species were the same.

Binding Sites↗
PubMed2005

Probabilistic segmentation and intensity estimation for microarray images.

We describe a probabilistic approach to simultaneous image segmentation and intensity estimation for complementary DNA microarray experiments. The approach overcomes several limitations of existing methods. In particular, it (a) uses a flexible Markov random field approach to segmentation that allows for a wider range of spot shapes than existing methods, including relatively common 'doughnut-shaped' spots; (b) models the image directly as background plus hybridization intensity, and estimates the two quantities simultaneously, avoiding the common logical error that estimates of foreground may be less than those of the corresponding background if the two are estimated separately; and (c) uses a probabilistic modeling approach to simultaneously perform segmentation and intensity estimation, and to compute spot quality measures. We describe two approaches to parameter estimation: a fast algorithm, based on the expectation-maximization and the iterated conditional modes algorithms, and a fully Bayesian framework. These approaches produce comparable results, and both appear to offer some advantages over other methods. We use an HIV experiment to compare our approach to two commercial software products: Spot and Arrayvision.

Algorithms↗